Paternal Song Complexity Predicts Offspring Sex Ratios Close to Fledging, but not Hatching, in Song Sparrows
Bibliographic record
Abstract
Sex allocation theory predicts that population sex ratios should be generally stable and close to unity, but individuals may benefit by adjusting the sex ratio of their offspring. For example, females paired with attractive males may benefit by overproducing sons relative to daughters, as sons inherit their fathers' attractive ornaments (“sexy son” hypothesis). Similarly, if compatible gene effects on fitness are more pronounced in males than females, genetically dissimilar mated pairs may enhance fitness by overproducing sons (“outbred son” hypothesis). We tested these hypotheses in Song Sparrows (Melospiza melodia) by examining offspring sex ratios of 64 complete families shortly after hatching (“early-stage”) and again shortly before fledging (“late-stage”) in relation to paternal song complexity and the genetic similarity of social mates. Neither early nor late-stage offspring sex ratio was related to parental genetic similarity. Nests of males with larger song repertoires contained more male-biased broods by the late-stage nestling period, but not in the early-stage nestling period. These findings suggest that attractive males may be better able to successfully raise male-biased broods, but not that females adaptively adjust primary sex ratios in response to their social mate's attractiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".